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I have a data.frame with a column that looks like that:
diagnosis
F.31.2,A.43.2,R.45.2,F.43.1
I want to somehow split this column into two colums with one containing all the values with F and one for all the other values, resulting in two columns in a df that looks like that.
F other
F.31.2,F43.1 A.43.2,R.45.2
Thanks in advance
Try next tidyverse approach. You can separate the rows by , and then create a group according to the pattern in order to reshape to wide and obtain the expected result:
library(dplyr)
library(tidyr)
#Data
df <- data.frame(diagnosis='F.31.2,A.43.2,R.45.2,F.43.1',stringsAsFactors = F)
#Code
new <- df %>% separate_rows(diagnosis,sep = ',') %>%
mutate(Group=ifelse(grepl('F',diagnosis),'F','Other')) %>%
pivot_wider(values_fn = toString,names_from=Group,values_from=diagnosis)
Output:
# A tibble: 1 x 2
F Other
<chr> <chr>
1 F.31.2, F.43.1 A.43.2, R.45.2
First, use strsplit at the commas. Then, using grep find indexes of F, and select/antiselect them by multiplying by 1 or -1 and paste them.
tmp <- el(strsplit(d$diagnosis, ","))
res <- lapply(c(1, -1), function(x) paste(tmp[grep("F", tmp)*x], collapse=","))
res <- setNames(as.data.frame(res), c("F", "other"))
res
# F other
# 1 F.31.2,F.43.1 A.43.2,R.45.2
Data:
d <- setNames(read.table(text="F.31.2,A.43.2,R.45.2,F.43.1"), "diagnosis")
I have a dataframe with a column full of text. It's hard to find any Regex pattern in it. What I am interested in though is to extract all countries based on a predefined list of countries I already have. I figured it shouldn't be too hard, but cannot find my way around str_extract to do this
Here's a replicable example:
data <- data.frame (text_column = c("I travelled to Germany last year, afterwards I visited Poland"," I enjoyed my vacation in Spain", "The weather in the Netherlands was not great"))
And I have a list of countries
country_vector <- c("Germany", "Poland","the Netherlands","France")
What I want is to match the text column against those countries, extract all matches and pivot them into separate columns for all matches. If no match, then we can enter NA.
So the expected outcome for this is:
Country_1 Country_2
Germany Poland
NA NA
the Netherlands NA
Given that there can be a multitude of countries mentioned in one column the ncol of the final tibble/df needs to be the length of the entry with the maximum number of countries.
Any idea how to tackle this? I've been able to do it for 1 entry but not for my entire dataframe. But I have the feeling I'm working in the wrong direction and there must be an easier solution.
temp <- stringr::str_extract(data$text_column,country_vector) %>%
as.data.frame() %>%
magrittr: set_colnames(c("countries")) %>%
dplyr::filter(!is.na(countries))
if (nrow(temp==0)){
temp <- temp %>% add_row(countries = NA) %>%
mutate(order = paste0("country_",seq(1:nrow(.)))) %>%
pivot_wider(.,names_from = order,values_from = countries)
print(temp)
} else {
temp <- temp %>%
mutate(order = paste0("country_",seq(1:nrow(.)))) %>%
pivot_wider(.,names_from = order,values_from = countries)
print(temp)
}
str_extract_all gives the data that you want :
library(stringr)
str_extract_all(data$text_column, str_c(country_vector, collapse = '|'))
#[[1]]
#[1] "Germany" "Poland"
#[[2]]
#character(0)
#[[3]]
#[1] "the Netherlands"
To get the data in the format that you want you need to manipulate the above output.
library(dplyr)
bind_rows(lapply(
str_extract_all(data$text_column, str_c(country_vector, collapse = '|')),
function(x) if(length(x)) as.data.frame(t(x)) else as.data.frame(t(NA))))
# V1 V2
#1 Germany Poland
#2 <NA> <NA>
#3 the Netherlands <NA>
There is a simplify option in str_extract_all, which returns a matrix. So, we can directly convert to a two column matrix and wrap with as.data.frame (if a data.frame is needed)
library(stringr)
as.data.frame(str_extract_all(data$text_column,
str_c(country_vector, collapse = '|'), simplify = TRUE))
# V1 V2
#1 Germany Poland
#2
#3 the Netherlands
I have a data.frame containing countries and cities in the column location, and I want to extract the former by matching with the world.cities$country.etc dataframe from library(maps) (or any other collection of country names).
Consider this example:
df <- data.frame(location = c("Aarup, Denmark",
"Switzerland",
"Estonia: Aaspere"),
other_col = c(2,3,4))
I attempted using this code
df %>% extract(location,
into = c("country", "rest_location"),
remove = FALSE,
function(x) x[which x %in% world.cities$country.etc])
But am not successful; I expect something like this:
location other_col country rest_location
1 Aarup, Denmark 2 Denmark Aarup,
2 Switzerland 3 Switzerland
3 Estonia: Aaspere 4 Estonia : Aaspere
We can create a pattern of all country names by pasting them together and use str_extract_all to get all the country names which match the pattern in location and remove the words which match the country names to get rest_location.
library(maps)
library(stringr)
all_countries <- str_c(unique(world.cities$country.etc), collapse = "|")
df$country <- sapply(str_extract_all(df$location, all_countries), toString)
df$rest_location <- str_remove_all(df$location, all_countries)
#OR can also do
#df$rest_location <- str_remove_all(df$location, df$country)
df
# location other_col country rest_location
#1 Aarup, Denmark 2 Denmark Aarup,
#2 Switzerland 3 Switzerland
#3 Estonia: Aaspere 4 Estonia : Aaspere
Using sapply and toString for country because if there are more than one country names in location they all are concatenated in one string.
you can try this as a starting point
library(tidyverse)
df %>%
rownames_to_column() %>%
separate_rows(location) %>%
mutate(gr = location %in% world.cities$country.etc) %>%
mutate(gr = ifelse(gr, "country", "rest_location")) %>%
spread(gr, location) %>%
right_join(df %>%
rownames_to_column(),
by = c("rowname", "other_col")) %>%
select(location, other_col, country, rest_location)
location other_col country rest_location
1 Aarup, Denmark 2 Denmark Aarup
2 Switzerland 3 Switzerland <NA>
3 Estonia: Aaspere 4 Estonia Aaspere
Of note, this only works if there are only two "words" in the location column. If necessary you have to specify a suitable separate e.g. sep=",|:"
Base R (not including maps package):
# Import the library:
library(maps)
# Split the string on the spaces:
country_city_vec <- strsplit(df$location, "\\s+")
# Replicate the other col's rows by the split string vec:
rolled_out_df <- data.frame(other_col = rep(df$other_col, sapply(country_city_vec, length)),
location = gsub("[[:punct:]]", "", unlist(country_city_vec)), stringsAsFactors = F)
# Match with the world df:
matched_with_world_df <- merge(df,
setNames(rolled_out_df[rolled_out_df$location %in% world.cities$country.etc,],
c("other_col", "country")),
by = "other_col", all.x = T)
# Extract the city/location drilldown:
matched_with_world_df$rest_location <- trimws(gsub("[[:punct:]]",
"",
gsub(paste0(matched_with_world_df$country,
collapse = "|"),
"", matched_with_world_df$location)), "both")
I am very new programming, and I am learning how to use dplyr, and I am wondering how to solve this problem:
I have this dataframe:
countries <- c("USA","Canada","Denmark","Albania", "Turkey","France", "Italy")
values <- c(1, 1, 3, 3,7,8,9)
old_df <- data.frame(countries, values, stringsAsFactors = FALSE)
I want to modify the order into my dataset to obtain this:
countries <- c("USA , Canada","Denmark , Albania", "Turkey","France", "Italy")
values <- c(1,3,7,8,9)
new_df <- data.frame(countries, values, stringsAsFactors = FALSE)
Because I am using dyplr I think that the best way to solve my problem could be:
library(dplyr)
new_df <- group_by(values) %>%
transmute(countries = countries) %>%
ungroup
Thank you in advance for any clue about how to solve this.
library(dplyr)
old_df %>%
group_by(values) %>%
summarise(countries = paste0(countries, collapse = ", "))
# # A tibble: 5 x 2
# values countries
# <dbl> <chr>
# 1 1 USA, Canada
# 2 3 Denmark, Albania
# 3 7 Turkey
# 4 8 France
# 5 9 Italy
The point here is that for each unique value in values you want to combine some of your rows, so you need to use summarise (i.e. you want to end up with one row per values value).
You can use summarise(countries = paste0(sort(countries), collapse = ", ")) if you want to apply an alphabetical order when you combine countries.
I feel like I have a bit of a complicated problem (or at least for me it is!).
I have a table of prices which will need to be read from a csv which will look exactly like this:
V1 <- c("","Destination","Spain","Spain","Spain","Portugal","Portugal","Portugal","Italy","Italy","Italy")
V2 <- c("","Min_Duration",rep(c(1,3,6),3))
V3 <- c("","Max_Duration",rep(c(2,5,10),3))
V4 <- c("Full-board","Level_1",runif(9,100,200))
V5 <- c("Full-board","Level_2",runif(9,201,500))
V6 <- c("Full-board","Level_3",runif(9,501,1000))
V7 <- c("Half-board","Level_1",runif(9,100,200))
V8 <- c("Half-board","Level_2",runif(9,201,500))
V9 <- c("Half-board","Level_3",runif(9,501,1000))
Lookup_matrix <- as.data.frame(cbind(V1,V2,V3,V4,V5,V6,V7,V8))
The prices in the above table will of course come out a bit strange as they're completely random - but we can ignore that...
I also have a table like this:
Destination <- c("Spain", "Italy", "Portugal")
Duration <- c(2,4,8)
Level <- c(1,3,3)
Board <- c("Half-board","Half-board","Full-board")
Price <- "Empty"
Price_matrix <- as.data.frame(cbind(Destination,Duration,Level,Board,Price))
My question is - how do I populate the 'Price' column of the price matrix with the corresponding prices that can be found in the lookup matrix? Please note that the duration variable of the price matrix will have to fit into a range found between the 'Min_Duration' and 'Max_Duration' columns in the lookup matrix.
In Excel I would use an Index,Match formula. But I'm stumped with R.
Thanks in advance,
Dan
Here is a tidyverse possibility
First, please note that I rename your input objects; both Price_matrix and Lookup_matrix are data.frames (not matrices).
df1 <- Price_matrix
df2 <- Lookup_matrix
Next we need to fix the column names of df2 = Lookup_matrix.
# Fix column names
colnames(df2) <- gsub("^_", "", apply(df2[1:2, ], 2, paste0, collapse = "_"))
df2 <- df2[-(1:2), ]
We now basically do a left join of df1 and df2; in order for df2 to be in a suitable format we spread data from wide to long, extract Price values for every Board and Level, and expand entries from Min_Duration to Max_Duration. Then we join by Destination, Duration, Level and Board.
Note that in your example, Destination = Italy has no Level = 3 entry in Lookup_matrix; we therefore get Price = NA for this entry.
library(tidyverse)
left_join(
df1 %>%
mutate_if(is.factor, as.character) %>%
select(-Price),
df2 %>%
mutate_if(is.factor, as.character) %>%
gather(key, Price, -Destination, -Min_Duration, -Max_Duration) %>%
separate(key, into = c("Board", "Level"), sep = "_", extra = "merge") %>%
mutate(Level = sub("Level_", "", Level)) %>%
rowwise() %>%
mutate(Duration = list(seq(as.numeric(Min_Duration), as.numeric(Max_Duration)))) %>%
unnest() %>%
select(-Min_Duration, -Max_Duration) %>%
mutate(Duration = as.character(Duration)))
#Joining, by = c("Destination", "Duration", "Level", "Board")
# Destination Duration Level Board Price
#1 Spain 2 1 Half-board 119.010942545719
#2 Italy 4 3 Half-board <NA>
#3 Portugal 8 3 Full-board 764.536124917446
Using datatable:
library(data.table)
nms = trimws(do.call(paste, transpose(Lookup_matrix[1:2, ])))# column names
cat(do.call(paste, c(collapse="\n", Lookup_matrix[-(1:2), ])), file = "mm.csv")
# Rewrite the data in the correct format. You do not have to.
# Just doing Lookup_matrix1 = setNames(Lookup_matrix[-(1:2),],nms) is enough
# but it will not have rectified the column classes.
Lookup_matrix1 = fread("mm.csv", col.names = nms)
melt(Lookup_matrix1, 1:3)[,
c("Board", "Level") := .(sub("[.]", "-", sub("\\.Leve.*", "", variable)), sub("\\D+", "", variable))][
Price_matrix[, -5], on=c("Destination", "Board", "Level", "Min_Duration <= Duration", "Max_Duration >= Duration")]
Destination Min_Duration Max_Duration variable value Board Level
1: Spain 2 2 Half.board.Level_1 105.2304 Half-board 1
2: Italy 4 4 <NA> NA Half-board 3
3: Portugal 8 8 Full.board.Level_3 536.5132 Full-board 3